US9516304B2 - System and method for improved optical character recognition for automated set-top box testing - Google Patents
System and method for improved optical character recognition for automated set-top box testing Download PDFInfo
- Publication number
- US9516304B2 US9516304B2 US13/989,777 US201113989777A US9516304B2 US 9516304 B2 US9516304 B2 US 9516304B2 US 201113989777 A US201113989777 A US 201113989777A US 9516304 B2 US9516304 B2 US 9516304B2
- Authority
- US
- United States
- Prior art keywords
- filter
- image
- images
- sequence
- filter configuration
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active, expires
Links
- 238000012360 testing method Methods 0.000 title claims abstract description 77
- 238000012015 optical character recognition Methods 0.000 title claims abstract description 42
- 238000000034 method Methods 0.000 title claims abstract description 38
- 238000007781 pre-processing Methods 0.000 claims description 7
- 239000003086 colorant Substances 0.000 claims description 4
- 238000001914 filtration Methods 0.000 claims description 2
- 230000008569 process Effects 0.000 abstract description 18
- 238000012549 training Methods 0.000 description 6
- 238000012545 processing Methods 0.000 description 5
- 238000004458 analytical method Methods 0.000 description 4
- 230000008859 change Effects 0.000 description 3
- 239000000047 product Substances 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 238000000342 Monte Carlo simulation Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 235000000332 black box Nutrition 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 239000007795 chemical reaction product Substances 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 238000011045 prefiltration Methods 0.000 description 1
- 230000005236 sound signal Effects 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/30—Noise filtering
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N17/00—Diagnosis, testing or measuring for television systems or their details
- H04N17/04—Diagnosis, testing or measuring for television systems or their details for receivers
- H04N17/045—Self-contained testing apparatus
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N17/00—Diagnosis, testing or measuring for television systems or their details
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
- G06V20/41—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N17/00—Diagnosis, testing or measuring for television systems or their details
- H04N17/04—Diagnosis, testing or measuring for television systems or their details for receivers
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N5/00—Details of television systems
- H04N5/44—Receiver circuitry for the reception of television signals according to analogue transmission standards
- H04N5/445—Receiver circuitry for the reception of television signals according to analogue transmission standards for displaying additional information
Definitions
- the present application pertains generally to the automated testing of set-top boxes (STB) and other audio visual equipment.
- the STB test system 20 comprises a controller 28 which manages the test function and interacts with the other features of the STB test system.
- the other features include an output interface for controlling a remote control device 12 , allowing commands to be sent to the STB and an input interface for receiving the video and/or audio signals from the STB 10 .
- This input interface may include an audio capture device 24 for accepting the audio as a test signal and/or a frame grabber 22 or similar device for accepting the video frames from the STB.
- the data captured is then made available to a processor for analysis, which in turn produces a test result and provides this to a user.
- the Test System issues a command or sequence of commands to the STB, suitably via the remote control interface.
- Each frame of video and/or the audio is captured and made to available to the Test System for analysis of the STB response to the commands issued to it.
- tests might include the generation of a “change channel” command to the STB followed by analysis of the audio and/or video outputs to ensure that the channel change command was correctly received and executed by the STB.
- OCR may be performed on a captured frame or a section of a frame.
- the person configuring the test will identify the section of the frame where text is to be found.
- the person may also configure the test by defining the expected result from the OCR process.
- the expected result may be a pre-determined known value or it may obtained elsewhere in the test routine.
- the OCR process may be employed to obtain and store information from the device under test.
- OCR is generally performed by a software component known as an “OCR Engine”.
- OCR Engine There are several OCR engines (software product) generally available for this purpose, i.e. the engines can process images and extract text from them.
- OCR engines conventionally come from the document processing area, which has several differences to images captured from a video stream.
- the resolution of document scanners used to capture document images is generally much greater than the resolution of images, even high definition images, captured from video.
- FIG. 1 is an illustration of an exemplary STB known in the art
- FIG. 2 is an illustration of a conventional prior art STB test system having OCR
- FIG. 3 is a block diagram of an aspect of STB test system according to an embodiment of the present application.
- FIG. 4 illustrates an exemplary arrangement for selecting a filter configuration according to another embodiment
- FIG. 5 illustrates a method for use in the arrangement of FIG. 4 .
- the present application is based on the premise that developing an OCR engine specifically for captured video images may be a costly and time consuming process. Instead, it is desirable to provide a method that improves the performance of existing OCR engines, for example which are intended for scanned documents.
- the present application improves the performance of OCR engines on captured video frames or sections thereof.
- the improvement is obtained by pre-processing the image (the captured frame or a part thereof) before submission to the OCR engine.
- it has been found by the present inventor that by processing an image with an image filter, the performance of the OCR engine may be improved.
- the difficulty is that whilst certain image filters may work perfectly in certain situations they may result in worse performance in others.
- An exemplary test system for testing a STB may generally employ the known STB test system of FIG. 2 and thus include a first interface for controlling the operation of a set-top box, for example by sending commands to the STB via an IR remote control.
- a first interface for controlling the operation of a set-top box, for example by sending commands to the STB via an IR remote control.
- other control inputs/interfaces may be employed for example a direct serial connection may be employed if the STB has such an interface available.
- an interface employed to control the STB is commonly employed in conventional test systems for STB's and thus their design and operation would be readily understood and familiar to those skilled in the art.
- the test system is configured to analyse video from the STB along with other outputs including for example audio.
- a second interface is employed to acquire one or more outputs from the STB.
- This second interface may include a frame grabber for capturing the video frames from the STB and/or an analog to digital converter for capturing the audio output from the STB.
- the frame grabber is synchronised to the video frame timing allowing it to capture one complete video frame at a time. It will be appreciated that where a digital output is available from the set-top box, the requirement for an analog frame grabber/audio capture device may be obviated and replaced by a digital interface which directly captures the video frames being transmitted.
- the video may be captured using a video camera directed at the display.
- the system may select a particular region of interest from the frame. The region of interest would typically be pre-defined during the configuration ⁇ setting up process of a test routine.
- an image processor (filter) 40 is provided to pre-filter the image (which as explained above may either be the entire captured frame or more likely a pre-selected section of the captured frame) before processing by the OCR engine to extract text from the image.
- the filter of the image processor 40 is a configurable filter such that the filter function applied to the image may be varied by the system. More specifically, the configuration for the filter for an image at a particular point in a test routine is suitably pre-set during an initial set-up configuration process where the test routine performed by the test system is prepared.
- the configurable filter 40 comprises a sequence of different image filters 42 a - f which may be applied sequentially to an image 44 from a captured frame or part thereof.
- each of the filters in the sequence may or may not be used to filter the image.
- the overall filter may be configured by the selection of which particular filters to use.
- each filter may be configurable by one or more parameters (shown by the numerals 0-5 for each filter), which adjust the filter characteristics. One of these parameters, e.g. 0 may indicate that the filter is not be used.
- the filtered image is passed to the OCR engine 24 for processing, where the recognised text may be compared with an expected result to determine the success or not of a particular test. In certain circumstances, there may not be an expected result and in this case, the result may simply be stored for reference or use subsequently in the test routine. This same routine may also be employed in the process to determine the filter configuration as described in greater detail below.
- each of these may be regarded as an image filter. It will be appreciated that a general parameter for a filter may be whether it is employed or not. A specific parameter in the case of removing one or more colour components would be the colour components to be removed, thus in the case of a RGB (Red Green Blue) image, the filter configurations might be removal of:
- an individual parameter might be whether to increase or decrease the contrast or the amount of contrast adjustment.
- the degree of blurring or sharpening would be an individual filter parameter.
- individual parameters may be the degree of scaling and/or the selection of a particular type of interpolation e.g. bi-cubic, linear etc.
- FIG. 5 An exemplary method of selecting a configuration for the filter is shown in FIG. 5 .
- a user when configuring the test system may optionally identify a section of the screen (captured frame) to be analysed.
- the user specifies the expected result of the OCR process.
- the expected result is used to compares the output ⁇ OCR result of different filter configurations on a sequence of images to determine the performance of the different filter configurations.
- Each of the sequence of images contains substantially the same text content, i.e. a user visually sees the same text in each image of the sequence. For example, capturing 100 sequential frames of video having an OSD (On Screen Display) box shown would have the same text content visible to a user in the box in each of the frames on the screen.
- OSD On Screen Display
- a bit for bit comparison of the captured frames may well show them to be completely different for the reasons explained above including noise and possibly changing video content behind the OSD box.
- a sequence of images are acquired as an initial step and the same sequence of images are used for comparing the performance of each filter configuration as the on-screen content may time out after a few seconds and an OSD disappear altogether. It will be appreciated that the sequence of images may be obtained as part of this process or they may have been obtained separately and stored.
- a first filter configuration is selected 52 to be used to filter the selected 54 first image in the sequence.
- the first filter configuration may have a “null” setting for each filter which does not alter the image. Pictorially, in FIG. 4 , this is represented by the setting “0” for a filter.
- This first filter configuration is used to set the filter and the first image is processed 56 by the filter using this configuration to provide a filtered image.
- the filtered image is then passed 58 through the OCR engine and the extracted text compared 60 with the expected result to determine the performance of the filter. It will be appreciated that the expected result need not be the entire of the extracted text but may in fact be a part of the text.
- the person configuring the test may simply require the presence of a particular word in the extracted text rather than an exact match to the whole of the extracted text.
- a decision 62 is then made based on the outcome, where the filter performance is negative (i.e. text not recognised correctly), then the next filter configuration may be selected 52 and the test repeated again. Where the filter performance is determined to be positive, the test may be repeated for the next image in the sequence 54 . Where the performance of a particular filter configuration is positive for all images in the sequence, then that configuration may be selected and stored 64 within the test routine for future use in a test process. At which point the configuration process for that aspect of the test is completed.
- all of the filters configurations may be tested with the accuracy of each determined (i.e. in how many of the sequence of images did a filter result in the OCR producing the expected result) and the filter with the best accuracy selected.
- sequence may not be the actual sequence of frames and the training set (sequence of images) may be for example be captured frames chosen at intervals apart.
- the filter configurations to be tested may be chosen in random order via traditional Monte Carlo methods, thus avoiding locally suboptimal blocks of filters if filters are tried in strict order of definition.
- the training set from the live signal, by capturing images “live” and accumulating these captures images on local storage. As long as each new image which arrives is recognised correctly, there is no need to re-train with all existing captured images (since these, by definition, will also have matched already). However once an image is captured which does not match the expected text, the captured images form the training set, and the algorithm starts the search for a better filter using the captured images. Where a further image does not match the expected result with a filter, it may be added to the training set until a suitable training set has been selected.
- This training may be time limited (so that if all images match the expected text for a user-defined time period, the current best filter (configuration) is judged good enough and saved).
- the system Given an image or set of images, it is possible for the system to automatically determine with a high degree of accuracy the expected text without user input. This is based on the observation that while incorrectly recognised text is typically random, the correctly recognised text is always the same. Therefore the system can “guess” that the string which appears most frequently in the recognition results is likely to be the text that the user wanted, and in the majority of cases it will be correct. Thus although the above method refers to the user entering an “expected” text result, it may not be necessary for the user to do this.
- the extracted text from a first image in a sequence may be employed as the “expected” result for the remainder of the images in the sequence, i.e. consistency of result is determined to equate to accuracy of result. It is possible of course that the OCR process may consistently fail to recognise the text correctly and that this alternative variation may not be appropriate.
- the presently described techniques may also be employed directly without a test configuration process.
- the above method has been described with respect to using an initial configuration routine to establish ⁇ store the correct filter parameters for performing a particular test.
- the method may also be used in a live scenario to determine text content from a sequence of captured images.
- a sequence of captured frames (or parts thereof) may be passed through the configurable filter using a first filter setting and then through the OCR engine to provide a text result.
- the text result may be regarded as valid.
- the process may be repeated with a different filter configuration. This process may be repeated, varying the configuration each time, until a valid result is determined. It will be appreciated that this process may be of use generally to video captured from a device under test and may be employed generally to identify text in video content.
Landscapes
- Engineering & Computer Science (AREA)
- Multimedia (AREA)
- Signal Processing (AREA)
- Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- General Health & Medical Sciences (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Biology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Computation (AREA)
- Bioinformatics & Cheminformatics (AREA)
- General Engineering & Computer Science (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Computational Linguistics (AREA)
- Software Systems (AREA)
- Testing, Inspecting, Measuring Of Stereoscopic Televisions And Televisions (AREA)
- Image Analysis (AREA)
- Character Input (AREA)
- Character Discrimination (AREA)
Abstract
Description
-
- Selectively removing one or more colour components from the original image, with or without conversion to greyscale of the resulting image.
- Adjusting image contrast
- Inverting the colours in the image
- Blurring the image
- Sharpening the image
- Zooming the image so that it is scaled up, interpolating pixels from the original image to create the new image.
-
- a) R
- b) G
- c) B
- d) RG
- e) RB
- f) GB
Claims (19)
Applications Claiming Priority (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
GB1020101.0 | 2010-11-26 | ||
GB1020101.0A GB2485833A (en) | 2010-11-26 | 2010-11-26 | Improved OCR Using Configurable Filtering for Analysing Set Top Boxes |
PCT/EP2011/073802 WO2012069662A1 (en) | 2010-11-26 | 2011-12-22 | Improved ocr for automated stb testing |
Related Parent Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/EP2011/073802 A-371-Of-International WO2012069662A1 (en) | 2010-11-26 | 2011-12-22 | Improved ocr for automated stb testing |
Related Child Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US15/338,983 Continuation US9942543B2 (en) | 2010-11-26 | 2016-10-31 | System and method for improved optical character recognition for automated set-top box testing |
Publications (2)
Publication Number | Publication Date |
---|---|
US20130347050A1 US20130347050A1 (en) | 2013-12-26 |
US9516304B2 true US9516304B2 (en) | 2016-12-06 |
Family
ID=43500715
Family Applications (2)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US13/989,777 Active 2032-09-10 US9516304B2 (en) | 2010-11-26 | 2011-12-22 | System and method for improved optical character recognition for automated set-top box testing |
US15/338,983 Active US9942543B2 (en) | 2010-11-26 | 2016-10-31 | System and method for improved optical character recognition for automated set-top box testing |
Family Applications After (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US15/338,983 Active US9942543B2 (en) | 2010-11-26 | 2016-10-31 | System and method for improved optical character recognition for automated set-top box testing |
Country Status (8)
Country | Link |
---|---|
US (2) | US9516304B2 (en) |
EP (1) | EP2643973B8 (en) |
BR (1) | BR112013013113B1 (en) |
CA (1) | CA2831113C (en) |
DK (1) | DK2643973T3 (en) |
GB (1) | GB2485833A (en) |
MY (1) | MY164774A (en) |
WO (1) | WO2012069662A1 (en) |
Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20210174427A1 (en) * | 2014-03-31 | 2021-06-10 | Monticello Enterprises LLC | System and method for providing a search entity-based payment process |
US11983759B2 (en) | 2014-03-31 | 2024-05-14 | Monticello Enterprises LLC | System and method for providing simplified in-store purchases and in-app purchases using a use-interface-based payment API |
US11989769B2 (en) | 2014-03-31 | 2024-05-21 | Monticello Enterprises LLC | System and method for providing simplified in-store, product-based and rental payment processes |
US12008629B2 (en) | 2014-03-31 | 2024-06-11 | Monticello Enterprises LLC | System and method for providing a social media shopping experience |
Families Citing this family (27)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2010092585A1 (en) * | 2009-02-16 | 2010-08-19 | Communitake Technologies Ltd. | A system, a method and a computer program product for automated remote control |
US9836376B2 (en) | 2009-09-24 | 2017-12-05 | Contec, Llc | Method and system for automated test of end-user devices |
GB2485833A (en) | 2010-11-26 | 2012-05-30 | S3 Res & Dev Ltd | Improved OCR Using Configurable Filtering for Analysing Set Top Boxes |
US10108515B2 (en) * | 2013-03-01 | 2018-10-23 | Sony Interactive Entertainment LLC | Remotely testing electronic devices using messaging and presence protocol |
GB2514410A (en) * | 2013-05-24 | 2014-11-26 | Ibm | Image scaling for images including low resolution text |
US10318804B2 (en) * | 2014-06-30 | 2019-06-11 | First American Financial Corporation | System and method for data extraction and searching |
US9460503B2 (en) | 2015-02-02 | 2016-10-04 | Arris Enterprises, Inc. | Automated video testing using QR codes embedded in a video stream |
US9960989B2 (en) | 2015-09-25 | 2018-05-01 | Contec, Llc | Universal device testing system |
US9900116B2 (en) | 2016-01-04 | 2018-02-20 | Contec, Llc | Test sequences using universal testing system |
US10122611B2 (en) | 2015-09-25 | 2018-11-06 | Contec, Llc | Universal device testing interface |
US9838295B2 (en) | 2015-11-23 | 2017-12-05 | Contec, Llc | Wireless routers under test |
US10291959B2 (en) | 2015-09-25 | 2019-05-14 | Contec, Llc | Set top boxes under test |
US9992084B2 (en) | 2015-11-20 | 2018-06-05 | Contec, Llc | Cable modems/eMTAs under test |
US10320651B2 (en) | 2015-10-30 | 2019-06-11 | Contec, Llc | Hardware architecture for universal testing system: wireless router test |
US10277497B2 (en) | 2015-09-25 | 2019-04-30 | Contec, Llc | Systems and methods for testing electronic devices using master-slave test architectures |
US9810735B2 (en) | 2015-09-25 | 2017-11-07 | Contec, Llc | Core testing machine |
US20170126536A1 (en) | 2015-10-30 | 2017-05-04 | Contec, Llc | Hardware Architecture for Universal Testing System: Cable Modem Test |
US9900113B2 (en) | 2016-02-29 | 2018-02-20 | Contec, Llc | Universal tester hardware |
US10462456B2 (en) | 2016-04-14 | 2019-10-29 | Contec, Llc | Automated network-based test system for set top box devices |
US10779056B2 (en) | 2016-04-14 | 2020-09-15 | Contec, Llc | Automated network-based test system for set top box devices |
US10237593B2 (en) | 2016-05-26 | 2019-03-19 | Telefonaktiebolaget Lm Ericsson (Publ) | Monitoring quality of experience (QoE) at audio/video (AV) endpoints using a no-reference (NR) method |
US10235260B1 (en) | 2016-07-26 | 2019-03-19 | Contec, Llc | Set top box and customer premise equipment (CPE) unit test controller |
US9848233B1 (en) | 2016-07-26 | 2017-12-19 | Contect, LLC | Set top box and customer premise equipment (CPE) unit test controller |
US9872070B1 (en) | 2016-07-26 | 2018-01-16 | Contec, Llc | Customer premise equipment (CPE) and set top box quality control test system providing scalability and performance |
US10230945B2 (en) | 2016-10-17 | 2019-03-12 | Accenture Global Solutions Limited | Dynamic loading and deployment of test files to prevent interruption of test execution |
US10284456B2 (en) | 2016-11-10 | 2019-05-07 | Contec, Llc | Systems and methods for testing electronic devices using master-slave test architectures |
KR102478396B1 (en) * | 2017-11-29 | 2022-12-19 | 삼성전자주식회사 | The Electronic Device Recognizing the Text in the Image |
Citations (23)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5262860A (en) * | 1992-04-23 | 1993-11-16 | International Business Machines Corporation | Method and system communication establishment utilizing captured and processed visually perceptible data within a broadcast video signal |
US5647023A (en) * | 1994-07-28 | 1997-07-08 | Lucent Technologies Inc. | Method of nonlinear filtering of degraded document images |
US5805747A (en) * | 1994-10-04 | 1998-09-08 | Science Applications International Corporation | Apparatus and method for OCR character and confidence determination using multiple OCR devices |
US6295387B1 (en) * | 1999-05-27 | 2001-09-25 | Lockheed Martin Corporation | Method and apparatus for determination of verified data |
US6658662B1 (en) * | 1997-06-30 | 2003-12-02 | Sun Microsystems, Inc. | Retrieving information from a broadcast signal |
US20040017944A1 (en) * | 2002-05-24 | 2004-01-29 | Xiaoging Ding | Method for character recognition based on gabor filters |
US6731788B1 (en) | 1999-01-28 | 2004-05-04 | Koninklijke Philips Electronics N.V. | Symbol Classification with shape features applied to neural network |
US20060171588A1 (en) * | 2005-01-28 | 2006-08-03 | Microsoft Corporation | Scalable hash-based character recognition |
US20060182311A1 (en) * | 2005-02-15 | 2006-08-17 | Dvpv, Ltd. | System and method of user interface and data entry from a video call |
US20070047819A1 (en) * | 2005-08-23 | 2007-03-01 | Hull Jonathan J | Data organization and access for mixed media document system |
US20070175998A1 (en) | 2005-09-01 | 2007-08-02 | Lev Zvi H | System and method for reliable content access using a cellular/wireless device with imaging capabilities |
US20080068503A1 (en) * | 2006-02-23 | 2008-03-20 | Funai Electric Co., Ltd. | Television Receiver |
US20080118162A1 (en) | 2006-11-20 | 2008-05-22 | Microsoft Corporation | Text Detection on Mobile Communications Devices |
CN101571922A (en) | 2008-05-04 | 2009-11-04 | 中兴通讯股份有限公司 | Character recognition tool for mobile terminal automation testing and method thereof |
US7664317B1 (en) * | 2006-03-23 | 2010-02-16 | Verizon Patent And Licensing Inc. | Video analysis |
US20110064304A1 (en) * | 2009-09-16 | 2011-03-17 | Hong Fu Jin Precision Industry (Shenzhen) Co., Ltd. | Electronic document comparison system and method |
US20110103688A1 (en) * | 2009-11-02 | 2011-05-05 | Harry Urbschat | System and method for increasing the accuracy of optical character recognition (OCR) |
US7995097B2 (en) * | 2007-05-25 | 2011-08-09 | Zoran Corporation | Techniques of motion estimation when acquiring an image of a scene that may be illuminated with a time varying luminance |
US20120020563A1 (en) * | 2010-07-23 | 2012-01-26 | International Business Machines Corporation | Systems and Methods for Automated Extraction of Measurement Information in Medical Videos |
US20120127522A1 (en) * | 2010-11-22 | 2012-05-24 | Ricoh Company, Ltd. | Information processing device, information processing method and recording medium |
US20120140081A1 (en) * | 2009-05-22 | 2012-06-07 | Stephen Clements | Test System For A Set-Top Box |
US20120162440A1 (en) * | 2010-12-23 | 2012-06-28 | The Directv Group, Inc. | System and method for performing an automated set top box test |
US8386121B1 (en) * | 2009-09-30 | 2013-02-26 | The United States Of America As Represented By The Administrator Of National Aeronautics And Space Administration | Optimized tuner selection for engine performance estimation |
Family Cites Families (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPH0358181A (en) * | 1989-07-26 | 1991-03-13 | Nec Corp | Optical character reader |
US5014328A (en) * | 1990-07-24 | 1991-05-07 | Eastman Kodak Company | Automatic detection and selection of a drop-out color used in conjunction with optical character recognition of preprinted forms |
JP4320438B2 (en) * | 2003-06-06 | 2009-08-26 | 独立行政法人 国立印刷局 | Character string extraction processing device for printed matter |
PL2472476T3 (en) * | 2005-06-10 | 2015-01-30 | Accenture Global Services Ltd | Electronic vehicle identification |
GB2485833A (en) | 2010-11-26 | 2012-05-30 | S3 Res & Dev Ltd | Improved OCR Using Configurable Filtering for Analysing Set Top Boxes |
-
2010
- 2010-11-26 GB GB1020101.0A patent/GB2485833A/en not_active Withdrawn
-
2011
- 2011-12-22 MY MYPI2013001902A patent/MY164774A/en unknown
- 2011-12-22 EP EP11805510.2A patent/EP2643973B8/en active Active
- 2011-12-22 US US13/989,777 patent/US9516304B2/en active Active
- 2011-12-22 DK DK11805510.2T patent/DK2643973T3/en active
- 2011-12-22 BR BR112013013113-6A patent/BR112013013113B1/en active IP Right Grant
- 2011-12-22 CA CA2831113A patent/CA2831113C/en active Active
- 2011-12-22 WO PCT/EP2011/073802 patent/WO2012069662A1/en active Application Filing
-
2016
- 2016-10-31 US US15/338,983 patent/US9942543B2/en active Active
Patent Citations (23)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5262860A (en) * | 1992-04-23 | 1993-11-16 | International Business Machines Corporation | Method and system communication establishment utilizing captured and processed visually perceptible data within a broadcast video signal |
US5647023A (en) * | 1994-07-28 | 1997-07-08 | Lucent Technologies Inc. | Method of nonlinear filtering of degraded document images |
US5805747A (en) * | 1994-10-04 | 1998-09-08 | Science Applications International Corporation | Apparatus and method for OCR character and confidence determination using multiple OCR devices |
US6658662B1 (en) * | 1997-06-30 | 2003-12-02 | Sun Microsystems, Inc. | Retrieving information from a broadcast signal |
US6731788B1 (en) | 1999-01-28 | 2004-05-04 | Koninklijke Philips Electronics N.V. | Symbol Classification with shape features applied to neural network |
US6295387B1 (en) * | 1999-05-27 | 2001-09-25 | Lockheed Martin Corporation | Method and apparatus for determination of verified data |
US20040017944A1 (en) * | 2002-05-24 | 2004-01-29 | Xiaoging Ding | Method for character recognition based on gabor filters |
US20060171588A1 (en) * | 2005-01-28 | 2006-08-03 | Microsoft Corporation | Scalable hash-based character recognition |
US20060182311A1 (en) * | 2005-02-15 | 2006-08-17 | Dvpv, Ltd. | System and method of user interface and data entry from a video call |
US20070047819A1 (en) * | 2005-08-23 | 2007-03-01 | Hull Jonathan J | Data organization and access for mixed media document system |
US20070175998A1 (en) | 2005-09-01 | 2007-08-02 | Lev Zvi H | System and method for reliable content access using a cellular/wireless device with imaging capabilities |
US20080068503A1 (en) * | 2006-02-23 | 2008-03-20 | Funai Electric Co., Ltd. | Television Receiver |
US7664317B1 (en) * | 2006-03-23 | 2010-02-16 | Verizon Patent And Licensing Inc. | Video analysis |
US20080118162A1 (en) | 2006-11-20 | 2008-05-22 | Microsoft Corporation | Text Detection on Mobile Communications Devices |
US7995097B2 (en) * | 2007-05-25 | 2011-08-09 | Zoran Corporation | Techniques of motion estimation when acquiring an image of a scene that may be illuminated with a time varying luminance |
CN101571922A (en) | 2008-05-04 | 2009-11-04 | 中兴通讯股份有限公司 | Character recognition tool for mobile terminal automation testing and method thereof |
US20120140081A1 (en) * | 2009-05-22 | 2012-06-07 | Stephen Clements | Test System For A Set-Top Box |
US20110064304A1 (en) * | 2009-09-16 | 2011-03-17 | Hong Fu Jin Precision Industry (Shenzhen) Co., Ltd. | Electronic document comparison system and method |
US8386121B1 (en) * | 2009-09-30 | 2013-02-26 | The United States Of America As Represented By The Administrator Of National Aeronautics And Space Administration | Optimized tuner selection for engine performance estimation |
US20110103688A1 (en) * | 2009-11-02 | 2011-05-05 | Harry Urbschat | System and method for increasing the accuracy of optical character recognition (OCR) |
US20120020563A1 (en) * | 2010-07-23 | 2012-01-26 | International Business Machines Corporation | Systems and Methods for Automated Extraction of Measurement Information in Medical Videos |
US20120127522A1 (en) * | 2010-11-22 | 2012-05-24 | Ricoh Company, Ltd. | Information processing device, information processing method and recording medium |
US20120162440A1 (en) * | 2010-12-23 | 2012-06-28 | The Directv Group, Inc. | System and method for performing an automated set top box test |
Cited By (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20210174427A1 (en) * | 2014-03-31 | 2021-06-10 | Monticello Enterprises LLC | System and method for providing a search entity-based payment process |
US11836784B2 (en) * | 2014-03-31 | 2023-12-05 | Monticello Enterprises LLC | System and method for providing a search entity-based payment process |
US11842380B2 (en) | 2014-03-31 | 2023-12-12 | Monticello Enterprises LLC | System and method for providing a social media shopping experience |
US11983759B2 (en) | 2014-03-31 | 2024-05-14 | Monticello Enterprises LLC | System and method for providing simplified in-store purchases and in-app purchases using a use-interface-based payment API |
US11989769B2 (en) | 2014-03-31 | 2024-05-21 | Monticello Enterprises LLC | System and method for providing simplified in-store, product-based and rental payment processes |
US12008629B2 (en) | 2014-03-31 | 2024-06-11 | Monticello Enterprises LLC | System and method for providing a social media shopping experience |
US12045868B2 (en) | 2014-03-31 | 2024-07-23 | Monticello Enterprises LLC | System and method for receiving data at a merchant device from a user device over a wireless link |
Also Published As
Publication number | Publication date |
---|---|
CA2831113A1 (en) | 2012-05-31 |
DK2643973T3 (en) | 2018-06-14 |
EP2643973B1 (en) | 2018-03-07 |
US9942543B2 (en) | 2018-04-10 |
BR112013013113B1 (en) | 2022-02-01 |
US20130347050A1 (en) | 2013-12-26 |
US20170048519A1 (en) | 2017-02-16 |
GB201020101D0 (en) | 2011-01-12 |
WO2012069662A1 (en) | 2012-05-31 |
CA2831113C (en) | 2017-05-09 |
BR112013013113A2 (en) | 2017-10-17 |
MY164774A (en) | 2018-01-30 |
GB2485833A (en) | 2012-05-30 |
EP2643973B8 (en) | 2020-11-11 |
EP2643973A1 (en) | 2013-10-02 |
WO2012069662A8 (en) | 2013-01-10 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US9942543B2 (en) | System and method for improved optical character recognition for automated set-top box testing | |
KR102402513B1 (en) | Method and apparatus for executing a content | |
CN105389809B (en) | Display performance testing method, system and device | |
MXPA05009704A (en) | Method of viewing audiovisual documents on a receiver, and receiver therefor. | |
EP3493064A1 (en) | A method and system for automated testing of a graphical user interface for customer devices | |
US20190230412A1 (en) | Method and device for configuring image mode | |
US8077997B2 (en) | Method and apparatus for image processing | |
JPWO2014013792A1 (en) | Noise evaluation method, image processing apparatus, imaging apparatus, and program | |
JP5335961B2 (en) | Display device and television receiver | |
US9386237B2 (en) | Image pickup apparatus and image pickup method | |
US11233985B2 (en) | Method for video quality detection and image processing circuit using the same | |
US20120008050A1 (en) | Video processing apparatus and video processing method | |
CN107992256B (en) | Window control method, device and system | |
CN112601080B (en) | Method for judging television monochromatic field signal | |
WO2021008187A1 (en) | Detection method and system for display interface switching function | |
KR101634652B1 (en) | Method and apparatus for intensificating contrast in image | |
CN114116464A (en) | Image processing test method and device | |
US20170116746A1 (en) | Method for segmenting a color image and digital microscope | |
JP4494424B2 (en) | Program and inspection device | |
US9582861B2 (en) | Image processing technique using high frequency data analysis to filter low frequency data by dynamic kernel adjustment | |
KR101397036B1 (en) | Method for outputting images and display unit enabling of the method | |
KR101378618B1 (en) | Image processing apparatus and method | |
CN105744119B (en) | Video playing device and ghost eliminating method thereof | |
JP5211864B2 (en) | Image processing device | |
CA3087909A1 (en) | Magnification enhancement of video for visually impaired viewers |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
FEPP | Fee payment procedure |
Free format text: PAYOR NUMBER ASSIGNED (ORIGINAL EVENT CODE: ASPN); ENTITY STATUS OF PATENT OWNER: LARGE ENTITY |
|
AS | Assignment |
Owner name: SILICON & SOFTWARE SYSTEMS LIMITED, IRELAND Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:FRIEL, LIAM;REEL/FRAME:036963/0971 Effective date: 20101011 |
|
AS | Assignment |
Owner name: S3 RESEARCH AND DEVELOPMENT LIMITED, IRELAND Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:SILICON & SOFTWARE SYSTEMS LIMITED;REEL/FRAME:037188/0821 Effective date: 20101117 |
|
AS | Assignment |
Owner name: SILICON & SOFTWARE SYSTEMS LIMITED, IRELAND Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:S3 RESEARCH AND DEVELOPMENT LIMITED;REEL/FRAME:037205/0450 Effective date: 20150904 |
|
AS | Assignment |
Owner name: S3 TV TECHNOLOGY LIMITED, IRELAND Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:SILICON & SOFTWARE SYSTEMS LIMITED;REEL/FRAME:037217/0649 Effective date: 20150904 |
|
AS | Assignment |
Owner name: ACCENTURE GLOBAL SOLUTIONS LIMITED, IRELAND Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:S3 TV TECHNOLOGY LIMITED;REEL/FRAME:037310/0843 Effective date: 20151130 |
|
STCF | Information on status: patent grant |
Free format text: PATENTED CASE |
|
MAFP | Maintenance fee payment |
Free format text: PAYMENT OF MAINTENANCE FEE, 4TH YEAR, LARGE ENTITY (ORIGINAL EVENT CODE: M1551); ENTITY STATUS OF PATENT OWNER: LARGE ENTITY Year of fee payment: 4 |
|
MAFP | Maintenance fee payment |
Free format text: PAYMENT OF MAINTENANCE FEE, 8TH YEAR, LARGE ENTITY (ORIGINAL EVENT CODE: M1552); ENTITY STATUS OF PATENT OWNER: LARGE ENTITY Year of fee payment: 8 |